Counting biological structures like cells in microscopy images is a crucial step to diagnose many diseases [1]} and to understand cellular and molecular mechanisms [2]}.
Several automatic cell counting methods have been proposed over the years to facilitate this tedious and challenging task.
Compared to a typical counting task, microscopy images present different challenges, such as low image contrast, significant cell shape and count variance, and superposition of cells, leading to occlusions.
As such, both detection-based and regression-based methods have been proposed, each with the advantages and the drawbacks already highlighted in sec:back:visual-counting. Basically, regression techniques have shown superior performance compared to the detection-based ones in crowded scenarios where the instances of the objects are sometimes not clearly visible due to occlusions and clumps; however, they cannot precisely localize the objects present in the scene, eventually providing only a coarse position of the area in which they are distributed.
A relevant example belonging to the detection-based category is [3]}, where the authors introduced a tree-structured discrete graphical model exploited to select and label a set of non-overlapping regions in the image by a global optimization of a classification score. More recently, the authors of [4]} exploited the popular Mask R-CNN [5]} instance segmentation framework to detect overlapping cells, whereas [6]} used a cnn to segment biological structures from 3D medical images. A comprehensive survey about deep learning algorithms used in medical image analysis, including cell detection and segmentation in microscopy images, is given by [7]}.
